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2010.02114
Cited By
Explaining The Efficacy of Counterfactually Augmented Data
5 October 2020
Divyansh Kaushik
Amrith Rajagopal Setlur
Eduard H. Hovy
Zachary Chase Lipton
CML
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Papers citing
"Explaining The Efficacy of Counterfactually Augmented Data"
29 / 29 papers shown
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Data Augmentations for Improved (Large) Language Model Generalization
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Out-of-Distribution Generalization in Text Classification: Past, Present, and Future
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Yangqiu Song
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Learning to Generalize for Cross-domain QA
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AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning
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29 Nov 2022
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
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Shuibai Zhang
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Hanmeng Liu
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Xingxu Xie
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44
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15 Nov 2022
Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection Task
Nyoungwoo Lee
chaeHun Park
Ho-Jin Choi
Jaegul Choo
27
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31 Oct 2022
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation
Phillip Howard
Gadi Singer
Vasudev Lal
Yejin Choi
Swabha Swayamdipta
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58
25
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22 Oct 2022
Augmentation by Counterfactual Explanation -- Fixing an Overconfident Classifier
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Nihal Murali
Forough Arabshahi
Sofia Triantafyllou
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59
4
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21 Oct 2022
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals
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Chris Develder
Thomas Demeester
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21 Oct 2022
On Feature Learning in the Presence of Spurious Correlations
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20 Oct 2022
FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition
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Lifan Yuan
Leyang Cui
Wen Gao
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24 Aug 2022
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models
Esma Balkir
S. Kiritchenko
I. Nejadgholi
Kathleen C. Fraser
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36
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08 Jun 2022
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection
Esma Balkir
I. Nejadgholi
Kathleen C. Fraser
S. Kiritchenko
FAtt
38
27
0
06 May 2022
Towards Fine-grained Causal Reasoning and QA
Linyi Yang
Zhen Wang
Yuxiang Wu
Jie Yang
Yue Zhang
41
15
0
15 Apr 2022
Informativeness and Invariance: Two Perspectives on Spurious Correlations in Natural Language
Jacob Eisenstein
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33
25
0
09 Apr 2022
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
Polina Kirichenko
Pavel Izmailov
A. Wilson
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49
318
0
06 Apr 2022
A Rationale-Centric Framework for Human-in-the-loop Machine Learning
Jinghui Lu
Linyi Yang
Brian Mac Namee
Yue Zhang
27
39
0
24 Mar 2022
Diffusion Causal Models for Counterfactual Estimation
Pedro Sanchez
Sotirios A. Tsaftaris
DiffM
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35
69
0
21 Feb 2022
Making a (Counterfactual) Difference One Rationale at a Time
Michael J. Plyler
Michal Green
Min Chi
21
11
0
13 Jan 2022
Connecting degree and polarity: An artificial language learning study
Lisa Bylinina
Alexey Tikhonov
Ekaterina Garmash
AI4CE
14
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0
13 Sep 2021
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
Amir Feder
Katherine A. Keith
Emaad A. Manzoor
Reid Pryzant
Dhanya Sridhar
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Roi Reichart
Margaret E. Roberts
Brandon M Stewart
Victor Veitch
Diyi Yang
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41
234
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02 Sep 2021
An Investigation of the (In)effectiveness of Counterfactually Augmented Data
Nitish Joshi
He He
OODD
19
46
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On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study
Divyansh Kaushik
Douwe Kiela
Zachary Chase Lipton
Wen-tau Yih
AAML
11
36
0
02 Jun 2021
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests
Victor Veitch
Alexander DÁmour
Steve Yadlowsky
Jacob Eisenstein
OOD
24
91
0
31 May 2021
A Survey of Data Augmentation Approaches for NLP
Steven Y. Feng
Varun Gangal
Jason W. Wei
Sarath Chandar
Soroush Vosoughi
Teruko Mitamura
Eduard H. Hovy
AIMat
39
799
0
07 May 2021
Hypothesis Only Baselines in Natural Language Inference
Adam Poliak
Jason Naradowsky
Aparajita Haldar
Rachel Rudinger
Benjamin Van Durme
190
576
0
02 May 2018
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Mohit Iyyer
John Wieting
Kevin Gimpel
Luke Zettlemoyer
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205
712
0
17 Apr 2018
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